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https://api.github.com/repos/huggingface/datasets/issues/171 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/171/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/171/comments | https://api.github.com/repos/huggingface/datasets/issues/171/events | https://github.com/huggingface/datasets/pull/171 | 621,199,128 | MDExOlB1bGxSZXF1ZXN0NDIwMjk0ODM0 | 171 | fix squad metric format | {
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"merged_at": "2020-05-22T13:36:48"... | The format of the squad metric was wrong.
This should fix #143
I tested with
```python3
predictions = [
{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}
]
references = [
{'answers': [{'text': 'Denver Broncos'}], 'id': '56be4db0acb8001400a502ec'}
]
``` | {
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"merged_at": "2020-05-20T12:23:07"... | What we have now as the `anli` dataset is actually the αNLI dataset from the ART challenge dataset. This name is confusing because `anli` is also the name of adversarial NLI (see [https://github.com/facebookresearch/anli](https://github.com/facebookresearch/anli)).
I renamed the current `anli` dataset by `art`. | {
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} | This PR adds the qanta question answering datasets from [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) and [Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples](https://www.aclweb.org/anthology/Q19-1029/) (adversarial fold)
This part... | {
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Code to reproduce (From example notebook):
import nlp
wikitext_dataset = nlp.load_dataset('wikitext')
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most rece... | {
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"merged_at": "2020-05-19T16:17:10"... | This PR separates AWS and Local tests to remove these ugly statements in the script:
```python
if "/" not in dataset_name:
logging.info("Skip {} because it is a canonical dataset")
return
```
To run a `aws` test, one should now run the following command:
```python
pytest -s... | {
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] | closed | false | null | [] | null | 4 | 2020-05-19T10:08:46 | 2020-06-23T15:07:33 | 2020-06-23T15:07:32 | MEMBER | null | null | null | Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.
Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-pl... | {
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For ANLI, use https://github.com/facebookresearch/anli. As that paper says, "Our dataset is not
to be confused with abductive NLI (Bhagavatula et al., 2019), which calls itself αNLI, or ART.".
Indeed, the paper cited under what is currently called anli says in the abstract "We int... | {
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] | closed | false | null | [] | null | 2 | 2020-05-18T22:18:21 | 2020-05-25T16:28:45 | 2020-05-25T16:28:45 | NONE | null | null | null | Hi guys,
In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks.
I can provide it in raw and preprocessed formats. | {
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"merged_at": "2020-05-18T21:36:20"... | Fix the `.map` issue in #160.
This makes sure it takes the previous files when computing the hash. | {
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"follo... | null | 12 | 2020-05-18T20:31:30 | 2020-05-24T18:10:03 | null | CONTRIBUTOR | null | null | null | Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp/utils/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers ... | {
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https://api.github.com/repos/huggingface/datasets/issues/160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/160/comments | https://api.github.com/repos/huggingface/datasets/issues/160/events | https://github.com/huggingface/datasets/issues/160 | 620,448,236 | MDU6SXNzdWU2MjA0NDgyMzY= | 160 | caching in map causes same result to be returned for train, validation and test | {
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I am working on a program that uses the `nlp` library with the `SST2` dataset.
The rough outline of the program is:
```
import nlp as nlp_datasets
...
parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')
...
dataset = nlp_datasets.load_dataset(*args.... | {
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} | This PR adds the Toronto Books Corpus.
.
It on consider TMX and plain text files (Moses) defined in the table **Statistics and TMX/Moses Downloads** [here](http://opus.nlpl.eu/Books.php ) | {
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"follo... | null | 11 | 2020-05-18T16:46:38 | 2020-06-05T08:08:58 | 2020-06-05T08:08:58 | NONE | null | null | null | I'm trying to load datasets from nlp but there seems to have error saying
"TypeError: list_() takes exactly one argument (2 given)"
gist can be found here
https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a | {
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"follo... | null | 7 | 2020-05-18T14:38:18 | 2020-07-23T16:41:55 | 2020-07-23T16:41:55 | NONE | null | null | null | The following snippet produces a syntax error:
```
import nlp
dataset = nlp.load_dataset('wmt14')
print(dataset['train'][0])
```
```
Traceback (most recent call last):
File "/home/tom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.... | {
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this PR adds the WNUT 17 dataset to `nlp`.
> Emerging and Rare entity recognition
> This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisati... | {
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In Colab,
```
%pip install -q nlp
%pip install -q apache_beam mwparserfromhell
dataset = nlp.load_dataset('wikipedia')
```
get
```
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/w... | {
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```python
data = nlp.load_dataset('imdb', cache_dir=data_cache)
f_half, s_half = train_test_split(data['train'], test_size=0.5, random_state=seed)... | {
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"merged_at": "2020-05-17T22:22:09"... | This PR adds [BERTScore](https://arxiv.org/abs/1904.09675) to metrics.
Here is an example of how to use it.
```sh
import nlp
bertscore = nlp.load_metric('metrics/bertscore') # or simply nlp.load_metric('bertscore') after this is added to huggingface's s3 bucket
predictions = ['example', 'fruit']
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"merged_at": "2020-05-16T13:44:33"... | AWS tests should in general not run for canonical datasets. Only local tests will run in this case. This way a PR is able to pass when adding a new dataset.
This PR changes to logic to the following:
1) All datasets that are present in `nlp/datasets` are tested only locally. This way when one adds a canonical da... | {
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```
ArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
This is how my predictions and references lo... | {
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"merged_at": "2020-05-17T12:18:20"... | This PR adds all wmt datasets scripts. At the moment the script is **not** functional for the language pairs "cs-en", "ru-en", "hi-en" because apparently it takes up to a week to get the manual data for these datasets: see http://ufal.mff.cuni.cz/czeng.
The datasets are fully functional though for the "big" languag... | {
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"merged_at": "2020-05-16T13:21:43"... | This PR also enables local tests by default
I think it's safer for now to enable both local and aws tests for every commit. The problem currently is that when we do a PR to add a dataset, the dataset is not yet on AWS on therefore not tested on the PR itself. Thus the PR will always be green even if the datasets are... | {
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"merged_at": "2020-05-16T13:56:22"... | Hi,
this PR adds the GermEval 2014 NER dataset 😃
> The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation [1] with the following properties:
> - The data was sampled from German Wikipedia and News Corpora as a collection of citations.
> - The dataset covers over 31,000... | {
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Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.
The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This... | {
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It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.
... | {
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More information and the download link are available [here](https://skylion007.github.io/OpenWebTextCorpus/). | {
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```python
dataset = nlp.load_dataset('glue')
```
The resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:
```python
metric = nlp.load_metric("glue")
```
which throws an error telling the user that they need to specify a task in GLUE. Should the... | {
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I am going through [this overview notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb)
In simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`
I get an error, which is connected with some inner code, I think:
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"merged_at": "2020-05-15T11:30:26"... | Remove webis from dataset folder.
Our first dataset script that only lives on AWS :-) https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/datasets/webis/tl_dr/?region=us-east-1 @julien-c @jplu | {
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"merged_at": "2020-05-15T10:37:02"... | I checked it with the data link of the mail you forwarded @thomwolf => works well! | {
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"merged_at": "2020-05-15T10:03:26"... | ## Change default Test from local => aws
As a default we set` aws=True`, `Local=False`, `slow=False`
### 1. RUN_AWS=1 (default)
This runs 4 tests per dataset script.
a) Does the dataset script have a valid etag / Can it be reached on AWS?
b) Can we load its `builder_class`?
c) Can we load **all** dataset c... | {
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[Here is the colab notebook I'm using.](https://colab.research.google.com/drive/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)
```python
import nlp
dataset = nlp.load_dataset('squad', split='validation[:10%]')
def test(sample):
samp... | {
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[Colab notebook](https://colab.research.google.com/drive/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)
But I meet this error :
> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
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Should I apply my map function on the subsets one by one ?
```python
import nlp
cnn_dm = nlp.load_dataset('cnn_dailymail')
for corpus in ['train', 'test', 'validation']:
cnn_dm[corpus] = cnn_dm[corpus].map(my_f... | {
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```python
dataset.drop('id')
```
But I didn't find how to remove a specific row.
**For example, how can I remove all sample w... | {
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"name": "metric bug",
"color": "25b21e",
"default": false,
"description": "A bug in a metric script"
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I have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.
I tried :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("test.pred.tokenized") as p, open("test.gold.tokenized") as g:
for lp, lg in zip(p, g):
... | {
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```python
cnn_dm = nlp.load_dataset('cnn_dailymail')
print(cnn_dm.info)
```
returns :
> AttributeError: 'dict' object has no attribute 'info' | {
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```python
import nlp
assert "cnn_dailymail" in [dataset.id for dataset in nlp.list_datasets()]
cnn_dm = nlp.load_dataset('cnn_dailymail')
```
[Colab notebook](https://colab.research.google.com/drive/1zQ3bYAVzm1h0mw0yWPqKAg_4EUlSx5Ex?usp=sharing)
gives following error ... | {
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"merged_at": "2020-05-15T09:16:42"... | Added dummy data test only for the first config. Will do the rest later.
I had to do add some minor hacks to an important function to make it work.
There might be a cleaner way to handle it - can you take a look @thomwolf ? | {
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"merged_at": "2020-05-14T14:27:40"... | Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.
The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.
The dataset ca... | {
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```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-... | {
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} | ### Better verifications when loading a dataset
I replaced the `urls_checksums` directory that used to contain `checksums.txt` and `cached_sizes.txt`, by a single file `dataset_infos.json`. It's just a dict `config_name` -> `DatasetInfo`.
It simplifies and improves how verifications of checksums and splits sizes ... | {
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} | WMT14 takes forever to download :-/
- WMT is the first dataset that uses an abstract class IMO, so I had to modify the `load_dataset_module` a bit. | {
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} | - Huge dataset - took ~1h to download
- Also this PR reformats all dataset scripts and adds `datasets` to `make style` | {
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} | Add a bunch of methods to easily list and inspect the processing scripts up-loaded on S3:
```python
nlp.list_datasets()
nlp.list_metrics()
# Copy and prepare the scripts at `local_path` for easy inspection/modification.
nlp.inspect_dataset(path, local_path)
# Copy and prepare the scripts at `local_path` for easy... | {
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} | This one is a beam dataset that downloads files using tensorflow.
I tested it on a small config and it works fine | {
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} | I just added the dummy data for this dataset.
This one was uses `tf.io.gfile.copy` to download the data but I added the support for custom download in the mock_download_manager. I also had to add a `tensorflow` dependency for tests. | {
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} | @thomwolf - After looking at `xnli` I think it's better to leave the translation features and add a `translation` key to make them work in our framework.
The result looks like this:
![Screenshot from 2020-05-12 18-34-43](https://user-images.githubusercontent.com/23423619/81721933-ee1faf00-9480-11ea-9e95-d6557cbd0c... | {
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} | Tests for py_utils functions and for the BaseReader used to read from arrow and parquet.
I also removed unused utils functions. | {
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} | ### Save size and number of examples
Now when you do `save_checksums`, it also create `cached_sizes.txt` right next to the checksum file.
This new file stores the bytes sizes and the number of examples of each split that has been prepared and stored in the cache. Example:
```
# Cached sizes: <full_config_name> <n... | {
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} | For now we will skip tests for Beam Datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/76 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/76/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/76/comments | https://api.github.com/repos/huggingface/datasets/issues/76/events | https://github.com/huggingface/datasets/pull/76 | 616,579,228 | MDExOlB1bGxSZXF1ZXN0NDE2NjYyMTk2 | 76 | pin flake 8 | {
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} | Flake 8's new version does not like our format. Pinning the version for now. | {
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https://api.github.com/repos/huggingface/datasets/issues/75 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/75/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/75/comments | https://api.github.com/repos/huggingface/datasets/issues/75/events | https://github.com/huggingface/datasets/pull/75 | 616,520,163 | MDExOlB1bGxSZXF1ZXN0NDE2NjE0MzU1 | 75 | WIP adding metrics | {
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} | Adding the following metrics as identified by @mariamabarham:
1. BLEU: BiLingual Evaluation Understudy: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py, https://github.com/chakki-works/sumeval/blob/master/sumeval/metrics/bleu.py (multilingual)
2. GLEU: Google-BLEU: https://github.com/cnap/gec-... | {
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https://api.github.com/repos/huggingface/datasets/issues/74 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/74/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/74/comments | https://api.github.com/repos/huggingface/datasets/issues/74/events | https://github.com/huggingface/datasets/pull/74 | 616,511,101 | MDExOlB1bGxSZXF1ZXN0NDE2NjA3MDcy | 74 | fix overflow check | {
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} | I did some tests and unfortunately the test
```
pa_array.nbytes > MAX_BATCH_BYTES
```
doesn't work. Indeed for a StructArray, `nbytes` can be less 2GB even if there is an overflow (it loops...).
I don't think we can do a proper overflow test for the limit of 2GB...
For now I replaced it with a sanity check on... | {
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https://api.github.com/repos/huggingface/datasets/issues/73 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/73/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/73/comments | https://api.github.com/repos/huggingface/datasets/issues/73/events | https://github.com/huggingface/datasets/pull/73 | 616,417,845 | MDExOlB1bGxSZXF1ZXN0NDE2NTMyMTg1 | 73 | JSON script | {
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} | Add a JSONS script to read JSON datasets from files. | {
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https://api.github.com/repos/huggingface/datasets/issues/72 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/72/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/72/comments | https://api.github.com/repos/huggingface/datasets/issues/72/events | https://github.com/huggingface/datasets/pull/72 | 616,225,010 | MDExOlB1bGxSZXF1ZXN0NDE2Mzc4Mjg4 | 72 | [README dummy data tests] README to better understand how the dummy data structure works | {
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} | In this PR a README.md is added to tests to shine more light on how the dummy data structure works. I try to explain the different possible cases. IMO the best way to understand the logic is to checkout the dummy data structure of the different datasets I mention in the README.md since those are the "edge cases".
@... | {
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